{
 "cells": [
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   "source": [
    "# Asynchronous Successive Halving\n",
    "\n",
    ":label:`sec_sh_async`\n",
    "\n",
    "As we have seen in :numref:`sec_rs_async`, we can accelerate HPO by\n",
    "distributing the evaluation of hyperparameter configurations across either\n",
    "multiple instances or multiples CPUs / GPUs on a single instance. However,\n",
    "compared to random search, it is not straightforward to run\n",
    "successive halving (SH) asynchronously in a distributed setting. Before we can\n",
    "decide which configuration to run next, we first have to collect all\n",
    "observations at the current rung level. This requires to\n",
    "synchronize workers at each rung level. For example, for the lowest rung level\n",
    "$r_{\\mathrm{min}}$, we first have to evaluate all $N = \\eta^K$ configurations, before we\n",
    "can promote the $\\frac{1}{\\eta}$ of them to the next rung level.\n",
    "\n",
    "In any distributed system, synchronization typically implies idle time for workers.\n",
    "First, we often observe high variations in training time across hyperparameter\n",
    "configurations. For example, assuming the number of filters per layer is a\n",
    "hyperparameter, then networks with less filters finish training faster than\n",
    "networks with more filters, which implies idle worker time due to stragglers.\n",
    "Moreover, the number of slots in a rung level is not always a multiple of the number\n",
    "of workers, in which case some workers may even sit idle for a full batch.\n",
    "\n",
    "Figure :numref:`synchronous_sh` shows the scheduling of synchronous SH with $\\eta=2$\n",
    "for four different trials with two workers. We start with evaluating Trial-0 and\n",
    "Trial-1 for one epoch and immediately continue with the next two trials once they\n",
    "are finished. We first have to wait until Trial-2 finishes, which takes\n",
    "substantially more time than the other trials, before we can promote the best two\n",
    "trials, i.e., Trial-0 and Trial-3 to the next rung level. This causes idle time for\n",
    "Worker-1. Then, we continue with Rung 1. Also, here Trial-3 takes longer than Trial-0,\n",
    "which leads to an additional ideling time of Worker-0. Once, we reach Rung-2, only\n",
    "the best trial, Trial-0, remains which occupies only one worker. To avoid that\n",
    "Worker-1 idles during that time, most implementaitons of SH continue already with\n",
    "the next round, and start evaluating new trials (e.g Trial-4) on the first rung.\n",
    "\n",
    "![Synchronous successive halving with two workers.](../img/sync_sh.svg)\n",
    ":label:`synchronous_sh`\n",
    "\n",
    "Asynchronous successive halving (ASHA) :cite:`li-arxiv18` adapts SH to the asynchronous\n",
    "parallel scenario. The main idea of ASHA is to promote configurations to the next rung\n",
    "level as soon as we collected at least $\\eta$ observations on the current rung level.\n",
    "This decision rule may lead to suboptimal promotions: configurations can be promoted to the\n",
    "next rung level, which in hindsight do not compare favourably against most others\n",
    "at the same rung level. On the other hand, we get rid of all synchronization points\n",
    "this way. In practice, such suboptimal initial promotions have only a modest impact on\n",
    "performance, not only because the ranking of hyperparameter configurations is often\n",
    "fairly consistent across rung levels, but also because rungs grow over time and\n",
    "reflect the distribution of metric values at this level better and better. If a\n",
    "worker is free, but no configuration can be promoted, we start a new configuration\n",
    "with $r = r_{\\mathrm{min}}$, i.e the first rung level.\n",
    "\n",
    ":numref:`asha` shows the scheduling of the same configurations for ASHA. Once Trial-1\n",
    "finishes, we collect the results of two trials (i.e Trial-0 and Trial-1) and\n",
    "immediately promote the better of them (Trial-0) to the next rung level. After Trial-0\n",
    "finishes on rung 1, there are too few trials there in order to support a further\n",
    "promotion. Hence, we continue with rung 0 and evaluate Trial-3. Once Trial-3 finishes,\n",
    "Trial-2 is still pending. At this point we have 3 trials evaluated on rung 0 and one\n",
    "trial evaluated already on rung 1. Since Trial-3 performs worse than Trial-0 at rung 0,\n",
    "and $\\eta=2$, we cannot promote any new trial yet, and Worker-1 starts Trial-4 from\n",
    "scratch instead. However, once Trial-2 finishes and\n",
    "scores worse than Trial-3, the latter is promoted towards rung 1. Afterwards, we\n",
    "collected 2 evaluations on rung 1, which means we can now promote Trial-0 towards\n",
    "rung 2. At the same time, Worker-1 continues with evaluating new trials (i.e.,\n",
    "Trial-5) on rung 0.\n",
    "\n",
    "\n",
    "![Asynchronous successive halving (ASHA) with two workers.](../img/asha.svg)\n",
    ":label:`asha`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0b0d01f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T20:01:48.151578Z",
     "iopub.status.busy": "2023-08-18T20:01:48.150780Z",
     "iopub.status.idle": "2023-08-18T20:01:51.996055Z",
     "shell.execute_reply": "2023-08-18T20:01:51.995043Z"
    },
    "origin_pos": 2,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:root:SageMakerBackend is not imported since dependencies are missing. You can install them with\n",
      "   pip install 'syne-tune[extra]'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AWS dependencies are not imported since dependencies are missing. You can install them with\n",
      "   pip install 'syne-tune[aws]'\n",
      "or (for everything)\n",
      "   pip install 'syne-tune[extra]'\n",
      "AWS dependencies are not imported since dependencies are missing. You can install them with\n",
      "   pip install 'syne-tune[aws]'\n",
      "or (for everything)\n",
      "   pip install 'syne-tune[extra]'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:root:Ray Tune schedulers and searchers are not imported since dependencies are missing. You can install them with\n",
      "   pip install 'syne-tune[raytune]'\n",
      "or (for everything)\n",
      "   pip install 'syne-tune[extra]'\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "from d2l import torch as d2l\n",
    "\n",
    "logging.basicConfig(level=logging.INFO)\n",
    "import matplotlib.pyplot as plt\n",
    "from syne_tune import StoppingCriterion, Tuner\n",
    "from syne_tune.backend.python_backend import PythonBackend\n",
    "from syne_tune.config_space import loguniform, randint\n",
    "from syne_tune.experiments import load_experiment\n",
    "from syne_tune.optimizer.baselines import ASHA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd0951d5",
   "metadata": {
    "origin_pos": 3
   },
   "source": [
    "## Objective Function\n",
    "\n",
    "We will use *Syne Tune* with the same objective function as in\n",
    ":numref:`sec_rs_async`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d1e0718f",
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "54"
    },
    "execution": {
     "iopub.execute_input": "2023-08-18T20:01:52.001567Z",
     "iopub.status.busy": "2023-08-18T20:01:52.000842Z",
     "iopub.status.idle": "2023-08-18T20:01:52.008420Z",
     "shell.execute_reply": "2023-08-18T20:01:52.007251Z"
    },
    "origin_pos": 4,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def hpo_objective_lenet_synetune(learning_rate, batch_size, max_epochs):\n",
    "    from syne_tune import Reporter\n",
    "    from d2l import torch as d2l\n",
    "\n",
    "    model = d2l.LeNet(lr=learning_rate, num_classes=10)\n",
    "    trainer = d2l.HPOTrainer(max_epochs=1, num_gpus=1)\n",
    "    data = d2l.FashionMNIST(batch_size=batch_size)\n",
    "    model.apply_init([next(iter(data.get_dataloader(True)))[0]], d2l.init_cnn)\n",
    "    report = Reporter()\n",
    "    for epoch in range(1, max_epochs + 1):\n",
    "        if epoch == 1:\n",
    "            # Initialize the state of Trainer\n",
    "            trainer.fit(model=model, data=data)\n",
    "        else:\n",
    "            trainer.fit_epoch()\n",
    "        validation_error = trainer.validation_error().cpu().detach().numpy()\n",
    "        report(epoch=epoch, validation_error=float(validation_error))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0b67805",
   "metadata": {
    "origin_pos": 5
   },
   "source": [
    "We will also use the same configuration space as before:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c6d2be2f",
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "55"
    },
    "execution": {
     "iopub.execute_input": "2023-08-18T20:01:52.013552Z",
     "iopub.status.busy": "2023-08-18T20:01:52.012730Z",
     "iopub.status.idle": "2023-08-18T20:01:52.017747Z",
     "shell.execute_reply": "2023-08-18T20:01:52.016801Z"
    },
    "origin_pos": 6,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "min_number_of_epochs = 2\n",
    "max_number_of_epochs = 10\n",
    "eta = 2\n",
    "\n",
    "config_space = {\n",
    "    \"learning_rate\": loguniform(1e-2, 1),\n",
    "    \"batch_size\": randint(32, 256),\n",
    "    \"max_epochs\": max_number_of_epochs,\n",
    "}\n",
    "initial_config = {\n",
    "    \"learning_rate\": 0.1,\n",
    "    \"batch_size\": 128,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86be45c9",
   "metadata": {
    "origin_pos": 7
   },
   "source": [
    "## Asynchronous Scheduler\n",
    "\n",
    "First, we define the number of workers that evaluate trials concurrently. We\n",
    "also need to specify how long we want to run random search, by defining an\n",
    "upper limit on the total wall-clock time.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "13a7bfc6",
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "56"
    },
    "execution": {
     "iopub.execute_input": "2023-08-18T20:01:52.022545Z",
     "iopub.status.busy": "2023-08-18T20:01:52.021981Z",
     "iopub.status.idle": "2023-08-18T20:01:52.025990Z",
     "shell.execute_reply": "2023-08-18T20:01:52.025119Z"
    },
    "origin_pos": 8,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "n_workers = 2  # Needs to be <= the number of available GPUs\n",
    "max_wallclock_time = 12 * 60  # 12 minutes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c88cc08",
   "metadata": {
    "origin_pos": 9
   },
   "source": [
    "The code for running ASHA is a simple variation of what we did for asynchronous\n",
    "random search.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "31550ee0",
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "56"
    },
    "execution": {
     "iopub.execute_input": "2023-08-18T20:01:52.030885Z",
     "iopub.status.busy": "2023-08-18T20:01:52.030158Z",
     "iopub.status.idle": "2023-08-18T20:01:52.038204Z",
     "shell.execute_reply": "2023-08-18T20:01:52.037151Z"
    },
    "origin_pos": 10,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:syne_tune.optimizer.schedulers.fifo:max_resource_level = 10, as inferred from config_space\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:syne_tune.optimizer.schedulers.fifo:Master random_seed = 3140976097\n"
     ]
    }
   ],
   "source": [
    "mode = \"min\"\n",
    "metric = \"validation_error\"\n",
    "resource_attr = \"epoch\"\n",
    "\n",
    "scheduler = ASHA(\n",
    "    config_space,\n",
    "    metric=metric,\n",
    "    mode=mode,\n",
    "    points_to_evaluate=[initial_config],\n",
    "    max_resource_attr=\"max_epochs\",\n",
    "    resource_attr=resource_attr,\n",
    "    grace_period=min_number_of_epochs,\n",
    "    reduction_factor=eta,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7397e21b",
   "metadata": {
    "origin_pos": 11
   },
   "source": [
    "Here, `metric` and `resource_attr` specify the key names used with the `report`\n",
    "callback, and `max_resource_attr` denotes which input to the objective function\n",
    "corresponds to $r_{\\mathrm{max}}$. Moreover, `grace_period` provides $r_{\\mathrm{min}}$, and\n",
    "`reduction_factor` is $\\eta$. We can run Syne Tune as before (this will\n",
    "take about 12 minutes):\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e359f0f1",
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "57"
    },
    "execution": {
     "iopub.execute_input": "2023-08-18T20:01:52.042832Z",
     "iopub.status.busy": "2023-08-18T20:01:52.041889Z",
     "iopub.status.idle": "2023-08-18T20:13:55.813788Z",
     "shell.execute_reply": "2023-08-18T20:13:55.812742Z"
    },
    "origin_pos": 12,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:syne_tune.tuner:results of trials will be saved on /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:root:Detected 4 GPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:root:running subprocess with command: /usr/bin/python /home/ci/.local/lib/python3.8/site-packages/syne_tune/backend/python_backend/python_entrypoint.py --learning_rate 0.1 --batch_size 128 --max_epochs 10 --tune_function_root /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/tune_function --tune_function_hash e03d187e043d2a17cae636d6af164015 --st_checkpoint_dir /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/0/checkpoints\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:syne_tune.tuner:(trial 0) - scheduled config {'learning_rate': 0.1, 'batch_size': 128, 'max_epochs': 10}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:root:running subprocess with command: /usr/bin/python /home/ci/.local/lib/python3.8/site-packages/syne_tune/backend/python_backend/python_entrypoint.py --learning_rate 0.44639554136672527 --batch_size 196 --max_epochs 10 --tune_function_root /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/tune_function --tune_function_hash e03d187e043d2a17cae636d6af164015 --st_checkpoint_dir /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/1/checkpoints\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:syne_tune.tuner:(trial 1) - scheduled config {'learning_rate': 0.44639554136672527, 'batch_size': 196, 'max_epochs': 10}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:root:running subprocess with command: /usr/bin/python /home/ci/.local/lib/python3.8/site-packages/syne_tune/backend/python_backend/python_entrypoint.py --learning_rate 0.011548051321691994 --batch_size 254 --max_epochs 10 --tune_function_root /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/tune_function --tune_function_hash e03d187e043d2a17cae636d6af164015 --st_checkpoint_dir /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/2/checkpoints\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:syne_tune.tuner:(trial 2) - scheduled config {'learning_rate': 0.011548051321691994, 'batch_size': 254, 'max_epochs': 10}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:root:running subprocess with command: /usr/bin/python /home/ci/.local/lib/python3.8/site-packages/syne_tune/backend/python_backend/python_entrypoint.py --learning_rate 0.14942487313193167 --batch_size 132 --max_epochs 10 --tune_function_root /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/tune_function --tune_function_hash e03d187e043d2a17cae636d6af164015 --st_checkpoint_dir /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/3/checkpoints\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:syne_tune.tuner:(trial 3) - scheduled config {'learning_rate': 0.14942487313193167, 'batch_size': 132, 'max_epochs': 10}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:syne_tune.tuner:Trial trial_id 1 completed.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:root:running subprocess with command: /usr/bin/python /home/ci/.local/lib/python3.8/site-packages/syne_tune/backend/python_backend/python_entrypoint.py --learning_rate 0.06317157191455719 --batch_size 242 --max_epochs 10 --tune_function_root /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/tune_function --tune_function_hash e03d187e043d2a17cae636d6af164015 --st_checkpoint_dir /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/4/checkpoints\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:syne_tune.tuner:(trial 4) - scheduled config {'learning_rate': 0.06317157191455719, 'batch_size': 242, 'max_epochs': 10}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:root:running subprocess with command: /usr/bin/python /home/ci/.local/lib/python3.8/site-packages/syne_tune/backend/python_backend/python_entrypoint.py --learning_rate 0.48801815412811467 --batch_size 41 --max_epochs 10 --tune_function_root /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/tune_function --tune_function_hash e03d187e043d2a17cae636d6af164015 --st_checkpoint_dir /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/5/checkpoints\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:syne_tune.tuner:(trial 5) - scheduled config {'learning_rate': 0.48801815412811467, 'batch_size': 41, 'max_epochs': 10}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:root:running subprocess with command: /usr/bin/python /home/ci/.local/lib/python3.8/site-packages/syne_tune/backend/python_backend/python_entrypoint.py --learning_rate 0.5904067586747807 --batch_size 244 --max_epochs 10 --tune_function_root /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/tune_function --tune_function_hash e03d187e043d2a17cae636d6af164015 --st_checkpoint_dir /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/6/checkpoints\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:syne_tune.tuner:(trial 6) - scheduled config {'learning_rate': 0.5904067586747807, 'batch_size': 244, 'max_epochs': 10}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:root:running subprocess with command: /usr/bin/python /home/ci/.local/lib/python3.8/site-packages/syne_tune/backend/python_backend/python_entrypoint.py --learning_rate 0.08812857364095393 --batch_size 148 --max_epochs 10 --tune_function_root /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/tune_function --tune_function_hash e03d187e043d2a17cae636d6af164015 --st_checkpoint_dir /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/7/checkpoints\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:syne_tune.tuner:(trial 7) - scheduled config {'learning_rate': 0.08812857364095393, 'batch_size': 148, 'max_epochs': 10}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:root:running subprocess with command: /usr/bin/python /home/ci/.local/lib/python3.8/site-packages/syne_tune/backend/python_backend/python_entrypoint.py --learning_rate 0.012271314788363914 --batch_size 235 --max_epochs 10 --tune_function_root /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/tune_function --tune_function_hash e03d187e043d2a17cae636d6af164015 --st_checkpoint_dir /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/8/checkpoints\n"
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      "INFO:syne_tune.tuner:(trial 8) - scheduled config {'learning_rate': 0.012271314788363914, 'batch_size': 235, 'max_epochs': 10}\n"
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      "INFO:syne_tune.tuner:Trial trial_id 5 completed.\n"
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      "INFO:root:running subprocess with command: /usr/bin/python /home/ci/.local/lib/python3.8/site-packages/syne_tune/backend/python_backend/python_entrypoint.py --learning_rate 0.08845692598296777 --batch_size 236 --max_epochs 10 --tune_function_root /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/tune_function --tune_function_hash e03d187e043d2a17cae636d6af164015 --st_checkpoint_dir /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/9/checkpoints\n"
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      "INFO:syne_tune.tuner:(trial 9) - scheduled config {'learning_rate': 0.08845692598296777, 'batch_size': 236, 'max_epochs': 10}\n"
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      "INFO:root:running subprocess with command: /usr/bin/python /home/ci/.local/lib/python3.8/site-packages/syne_tune/backend/python_backend/python_entrypoint.py --learning_rate 0.0825770880068151 --batch_size 75 --max_epochs 10 --tune_function_root /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/tune_function --tune_function_hash e03d187e043d2a17cae636d6af164015 --st_checkpoint_dir /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/10/checkpoints\n"
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      "INFO:syne_tune.tuner:(trial 10) - scheduled config {'learning_rate': 0.0825770880068151, 'batch_size': 75, 'max_epochs': 10}\n"
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      "INFO:root:running subprocess with command: /usr/bin/python /home/ci/.local/lib/python3.8/site-packages/syne_tune/backend/python_backend/python_entrypoint.py --learning_rate 0.20235201406823256 --batch_size 65 --max_epochs 10 --tune_function_root /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/tune_function --tune_function_hash e03d187e043d2a17cae636d6af164015 --st_checkpoint_dir /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/11/checkpoints\n"
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      "INFO:syne_tune.tuner:(trial 11) - scheduled config {'learning_rate': 0.20235201406823256, 'batch_size': 65, 'max_epochs': 10}\n"
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      "INFO:syne_tune.tuner:(trial 12) - scheduled config {'learning_rate': 0.3359885631737537, 'batch_size': 58, 'max_epochs': 10}\n"
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      "INFO:root:running subprocess with command: /usr/bin/python /home/ci/.local/lib/python3.8/site-packages/syne_tune/backend/python_backend/python_entrypoint.py --learning_rate 0.7892434579795236 --batch_size 89 --max_epochs 10 --tune_function_root /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/tune_function --tune_function_hash e03d187e043d2a17cae636d6af164015 --st_checkpoint_dir /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/13/checkpoints\n"
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      "INFO:syne_tune.tuner:(trial 13) - scheduled config {'learning_rate': 0.7892434579795236, 'batch_size': 89, 'max_epochs': 10}\n"
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      "INFO:root:running subprocess with command: /usr/bin/python /home/ci/.local/lib/python3.8/site-packages/syne_tune/backend/python_backend/python_entrypoint.py --learning_rate 0.1233786579597858 --batch_size 176 --max_epochs 10 --tune_function_root /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/tune_function --tune_function_hash e03d187e043d2a17cae636d6af164015 --st_checkpoint_dir /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/14/checkpoints\n"
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      "INFO:syne_tune.tuner:(trial 14) - scheduled config {'learning_rate': 0.1233786579597858, 'batch_size': 176, 'max_epochs': 10}\n"
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      "INFO:syne_tune.tuner:Trial trial_id 13 completed.\n"
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      "INFO:root:running subprocess with command: /usr/bin/python /home/ci/.local/lib/python3.8/site-packages/syne_tune/backend/python_backend/python_entrypoint.py --learning_rate 0.13707981127012328 --batch_size 141 --max_epochs 10 --tune_function_root /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/tune_function --tune_function_hash e03d187e043d2a17cae636d6af164015 --st_checkpoint_dir /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/15/checkpoints\n"
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      "INFO:syne_tune.tuner:(trial 15) - scheduled config {'learning_rate': 0.13707981127012328, 'batch_size': 141, 'max_epochs': 10}\n"
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      "INFO:root:running subprocess with command: /usr/bin/python /home/ci/.local/lib/python3.8/site-packages/syne_tune/backend/python_backend/python_entrypoint.py --learning_rate 0.02913976299993913 --batch_size 116 --max_epochs 10 --tune_function_root /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/tune_function --tune_function_hash e03d187e043d2a17cae636d6af164015 --st_checkpoint_dir /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/16/checkpoints\n"
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      "INFO:syne_tune.tuner:(trial 16) - scheduled config {'learning_rate': 0.02913976299993913, 'batch_size': 116, 'max_epochs': 10}\n"
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      "INFO:root:running subprocess with command: /usr/bin/python /home/ci/.local/lib/python3.8/site-packages/syne_tune/backend/python_backend/python_entrypoint.py --learning_rate 0.033362897489792855 --batch_size 154 --max_epochs 10 --tune_function_root /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/tune_function --tune_function_hash e03d187e043d2a17cae636d6af164015 --st_checkpoint_dir /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/17/checkpoints\n"
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      "INFO:syne_tune.tuner:(trial 17) - scheduled config {'learning_rate': 0.033362897489792855, 'batch_size': 154, 'max_epochs': 10}\n"
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      "INFO:root:running subprocess with command: /usr/bin/python /home/ci/.local/lib/python3.8/site-packages/syne_tune/backend/python_backend/python_entrypoint.py --learning_rate 0.29442952580755816 --batch_size 210 --max_epochs 10 --tune_function_root /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/tune_function --tune_function_hash e03d187e043d2a17cae636d6af164015 --st_checkpoint_dir /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/18/checkpoints\n"
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      "INFO:syne_tune.tuner:(trial 18) - scheduled config {'learning_rate': 0.29442952580755816, 'batch_size': 210, 'max_epochs': 10}\n"
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      "INFO:root:running subprocess with command: /usr/bin/python /home/ci/.local/lib/python3.8/site-packages/syne_tune/backend/python_backend/python_entrypoint.py --learning_rate 0.10214259921521483 --batch_size 239 --max_epochs 10 --tune_function_root /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/tune_function --tune_function_hash e03d187e043d2a17cae636d6af164015 --st_checkpoint_dir /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/19/checkpoints\n"
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      "INFO:syne_tune.tuner:(trial 19) - scheduled config {'learning_rate': 0.10214259921521483, 'batch_size': 239, 'max_epochs': 10}\n"
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    },
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     "output_type": "stream",
     "text": [
      "INFO:syne_tune.tuner:tuning status (last metric is reported)\n",
      " trial_id     status  iter  learning_rate  batch_size  max_epochs  epoch  validation_error  worker-time\n",
      "        0    Stopped     4       0.100000         128          10    4.0          0.430578    29.093798\n",
      "        1  Completed    10       0.446396         196          10   10.0          0.205652    72.747496\n",
      "        2    Stopped     2       0.011548         254          10    2.0          0.900570    13.729115\n",
      "        3    Stopped     8       0.149425         132          10    8.0          0.259171    58.980305\n",
      "        4    Stopped     4       0.063172         242          10    4.0          0.900579    27.773950\n",
      "        5  Completed    10       0.488018          41          10   10.0          0.140488   113.171314\n",
      "        6    Stopped    10       0.590407         244          10   10.0          0.193776    70.364757\n",
      "        7    Stopped     2       0.088129         148          10    2.0          0.899955    14.169738\n",
      "        8    Stopped     2       0.012271         235          10    2.0          0.899840    13.434274\n",
      "        9    Stopped     2       0.088457         236          10    2.0          0.899801    13.034437\n",
      "       10    Stopped     4       0.082577          75          10    4.0          0.385970    35.426524\n",
      "       11    Stopped     4       0.202352          65          10    4.0          0.543102    34.653495\n",
      "       12    Stopped    10       0.335989          58          10   10.0          0.149558    90.924182\n",
      "       13  Completed    10       0.789243          89          10   10.0          0.144887    77.365970\n",
      "       14    Stopped     2       0.123379         176          10    2.0          0.899987    12.422906\n",
      "       15    Stopped     2       0.137080         141          10    2.0          0.899983    13.395153\n",
      "       16    Stopped     4       0.029140         116          10    4.0          0.900532    27.834111\n",
      "       17    Stopped     2       0.033363         154          10    2.0          0.899996    13.407285\n",
      "       18 InProgress     1       0.294430         210          10    1.0          0.899878     6.126259\n",
      "       19 InProgress     0       0.102143         239          10      -                 -            -\n",
      "2 trials running, 18 finished (3 until the end), 437.07s wallclock-time\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "INFO:root:running subprocess with command: /usr/bin/python /home/ci/.local/lib/python3.8/site-packages/syne_tune/backend/python_backend/python_entrypoint.py --learning_rate 0.02846298236356246 --batch_size 115 --max_epochs 10 --tune_function_root /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/tune_function --tune_function_hash e03d187e043d2a17cae636d6af164015 --st_checkpoint_dir /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/20/checkpoints\n"
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      "INFO:syne_tune.tuner:(trial 20) - scheduled config {'learning_rate': 0.02846298236356246, 'batch_size': 115, 'max_epochs': 10}\n"
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      "INFO:root:running subprocess with command: /usr/bin/python /home/ci/.local/lib/python3.8/site-packages/syne_tune/backend/python_backend/python_entrypoint.py --learning_rate 0.037703019195187606 --batch_size 91 --max_epochs 10 --tune_function_root /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/tune_function --tune_function_hash e03d187e043d2a17cae636d6af164015 --st_checkpoint_dir /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/21/checkpoints\n"
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      "INFO:syne_tune.tuner:(trial 21) - scheduled config {'learning_rate': 0.037703019195187606, 'batch_size': 91, 'max_epochs': 10}\n"
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      "INFO:syne_tune.tuner:(trial 22) - scheduled config {'learning_rate': 0.0741039859356903, 'batch_size': 192, 'max_epochs': 10}\n"
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      "INFO:syne_tune.tuner:(trial 23) - scheduled config {'learning_rate': 0.3032613031191755, 'batch_size': 252, 'max_epochs': 10}\n"
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      "INFO:syne_tune.tuner:(trial 24) - scheduled config {'learning_rate': 0.019823425532533637, 'batch_size': 252, 'max_epochs': 10}\n"
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      "INFO:syne_tune.tuner:(trial 25) - scheduled config {'learning_rate': 0.8203370335228594, 'batch_size': 77, 'max_epochs': 10}\n"
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      "INFO:syne_tune.tuner:(trial 26) - scheduled config {'learning_rate': 0.2960420911378594, 'batch_size': 104, 'max_epochs': 10}\n"
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      "INFO:syne_tune.tuner:(trial 27) - scheduled config {'learning_rate': 0.2993874715754653, 'batch_size': 192, 'max_epochs': 10}\n"
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      "INFO:syne_tune.tuner:(trial 28) - scheduled config {'learning_rate': 0.08056711961080017, 'batch_size': 36, 'max_epochs': 10}\n"
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      "INFO:syne_tune.tuner:(trial 29) - scheduled config {'learning_rate': 0.26868380288030347, 'batch_size': 151, 'max_epochs': 10}\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:syne_tune.tuner:Trial trial_id 29 completed.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:root:running subprocess with command: /usr/bin/python /home/ci/.local/lib/python3.8/site-packages/syne_tune/backend/python_backend/python_entrypoint.py --learning_rate 0.9197404791177789 --batch_size 66 --max_epochs 10 --tune_function_root /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/tune_function --tune_function_hash e03d187e043d2a17cae636d6af164015 --st_checkpoint_dir /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046/30/checkpoints\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:syne_tune.tuner:(trial 30) - scheduled config {'learning_rate': 0.9197404791177789, 'batch_size': 66, 'max_epochs': 10}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:syne_tune.stopping_criterion:reaching max wallclock time (720), stopping there.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:syne_tune.tuner:Stopping trials that may still be running.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:syne_tune.tuner:Tuning finished, results of trials can be found on /home/ci/syne-tune/python-entrypoint-2023-08-18-20-01-52-046\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------\n",
      "Resource summary (last result is reported):\n",
      " trial_id     status  iter  learning_rate  batch_size  max_epochs  epoch  validation_error  worker-time\n",
      "        0    Stopped     4       0.100000         128          10      4          0.430578    29.093798\n",
      "        1  Completed    10       0.446396         196          10     10          0.205652    72.747496\n",
      "        2    Stopped     2       0.011548         254          10      2          0.900570    13.729115\n",
      "        3    Stopped     8       0.149425         132          10      8          0.259171    58.980305\n",
      "        4    Stopped     4       0.063172         242          10      4          0.900579    27.773950\n",
      "        5  Completed    10       0.488018          41          10     10          0.140488   113.171314\n",
      "        6    Stopped    10       0.590407         244          10     10          0.193776    70.364757\n",
      "        7    Stopped     2       0.088129         148          10      2          0.899955    14.169738\n",
      "        8    Stopped     2       0.012271         235          10      2          0.899840    13.434274\n",
      "        9    Stopped     2       0.088457         236          10      2          0.899801    13.034437\n",
      "       10    Stopped     4       0.082577          75          10      4          0.385970    35.426524\n",
      "       11    Stopped     4       0.202352          65          10      4          0.543102    34.653495\n",
      "       12    Stopped    10       0.335989          58          10     10          0.149558    90.924182\n",
      "       13  Completed    10       0.789243          89          10     10          0.144887    77.365970\n",
      "       14    Stopped     2       0.123379         176          10      2          0.899987    12.422906\n",
      "       15    Stopped     2       0.137080         141          10      2          0.899983    13.395153\n",
      "       16    Stopped     4       0.029140         116          10      4          0.900532    27.834111\n",
      "       17    Stopped     2       0.033363         154          10      2          0.899996    13.407285\n",
      "       18    Stopped     8       0.294430         210          10      8          0.241193    52.089688\n",
      "       19    Stopped     2       0.102143         239          10      2          0.900002    12.487762\n",
      "       20    Stopped     2       0.028463         115          10      2          0.899995    14.100359\n",
      "       21    Stopped     2       0.037703          91          10      2          0.900026    14.664848\n",
      "       22    Stopped     2       0.074104         192          10      2          0.901730    13.312770\n",
      "       23    Stopped     2       0.303261         252          10      2          0.900009    12.725821\n",
      "       24    Stopped     2       0.019823         252          10      2          0.899917    12.533380\n",
      "       25    Stopped    10       0.820337          77          10     10          0.196842    81.816103\n",
      "       26    Stopped    10       0.296042         104          10     10          0.198453    81.121330\n",
      "       27    Stopped     4       0.299387         192          10      4          0.336183    24.610689\n",
      "       28 InProgress     9       0.080567          36          10      9          0.203052   104.303746\n",
      "       29  Completed    10       0.268684         151          10     10          0.222814    68.217289\n",
      "       30 InProgress     1       0.919740          66          10      1          0.900037    10.070776\n",
      "2 trials running, 29 finished (4 until the end), 723.70s wallclock-time\n",
      "\n",
      "validation_error: best 0.1404876708984375 for trial-id 5\n",
      "--------------------\n"
     ]
    }
   ],
   "source": [
    "trial_backend = PythonBackend(\n",
    "    tune_function=hpo_objective_lenet_synetune,\n",
    "    config_space=config_space,\n",
    ")\n",
    "\n",
    "stop_criterion = StoppingCriterion(max_wallclock_time=max_wallclock_time)\n",
    "tuner = Tuner(\n",
    "    trial_backend=trial_backend,\n",
    "    scheduler=scheduler,\n",
    "    stop_criterion=stop_criterion,\n",
    "    n_workers=n_workers,\n",
    "    print_update_interval=int(max_wallclock_time * 0.6),\n",
    ")\n",
    "tuner.run()"
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  },
  {
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   "id": "02c8a8c9",
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   "source": [
    "Note that we are running a variant of ASHA where underperforming trials are\n",
    "stopped early. This is different to our implementation in\n",
    ":numref:`sec_mf_hpo_sh`, where each training job is started with a fixed\n",
    "`max_epochs`. In the latter case, a well-performing trial which reaches the\n",
    "full 10 epochs, first needs to train 1, then 2, then 4, then 8 epochs, each\n",
    "time starting from scratch. This type of pause-and-resume scheduling can be\n",
    "implemented efficiently by checkpointing the training state after each epoch,\n",
    "but we avoid this extra complexity here. After the experiment has finished,\n",
    "we can retrieve and plot results.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dcb9f8b5",
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    "execution": {
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     "iopub.status.busy": "2023-08-18T20:13:55.817301Z",
     "iopub.status.idle": "2023-08-18T20:13:56.081185Z",
     "shell.execute_reply": "2023-08-18T20:13:56.080250Z"
    },
    "origin_pos": 14,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
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   "source": [
    "d2l.set_figsize()\n",
    "e = load_experiment(tuner.name)\n",
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    "## Visualize the Optimization Process\n",
    "\n",
    "Once more, we visualize the learning curves of every trial (each color in the plot represents a trial). Compare this to\n",
    "asynchronous random search in :numref:`sec_rs_async`. As we have seen for\n",
    "successive halving in :numref:`sec_mf_hpo`, most of the trials are stopped\n",
    "at 1 or 2 epochs ($r_{\\mathrm{min}}$ or $\\eta * r_{\\mathrm{min}}$). However, trials do not stop\n",
    "at the same point, because they require different amount of time per epoch. If\n",
    "we ran standard successive halving instead of ASHA, we would need to synchronize\n",
    "our workers, before we can promote configurations to the next rung level.\n"
   ]
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    "tab": [
     "pytorch"
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   ],
   "source": [
    "d2l.set_figsize([6, 2.5])\n",
    "results = e.results\n",
    "for trial_id in results.trial_id.unique():\n",
    "    df = results[results[\"trial_id\"] == trial_id]\n",
    "    d2l.plt.plot(\n",
    "        df[\"st_tuner_time\"],\n",
    "        df[\"validation_error\"],\n",
    "        marker=\"o\"\n",
    "    )\n",
    "d2l.plt.xlabel(\"wall-clock time\")\n",
    "d2l.plt.ylabel(\"objective function\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c13b3389",
   "metadata": {
    "origin_pos": 17
   },
   "source": [
    "## Summary\n",
    "\n",
    "Compared to random search, successive halving is not quite as trivial to run in\n",
    "an asynchronous distributed setting. To avoid synchronisation points, we promote\n",
    "configurations as quickly as possible to the next rung level, even if this means\n",
    "promoting some wrong ones. In practice, this usually does not hurt much, and the\n",
    "gains of asynchronous versus synchronous scheduling are usually much higher\n",
    "than the loss of the suboptimal decision making.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f3eebc1",
   "metadata": {
    "origin_pos": 18,
    "tab": [
     "pytorch"
    ]
   },
   "source": [
    "[Discussions](https://discuss.d2l.ai/t/12101)\n"
   ]
  }
 ],
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